SoroushVahidi/scidocs huggingface dataset:
The Mini Nutritional Assessment (MNA) review of the literature--What does it tell us?
(2006)

UNLABELLED
To review the literature on the MNA to Spring 2006, we searched MEDLINE, Web of Science and Scopus, and did a manual search in J Nutr Health Aging, Clin Nutr, Eur J Clin Nutr and free online available publications.


VALIDATION AND VALIDITY
The MNA was validated against two principal criteria, clinical status and comprehensive nutrition assessment using principal component and discriminant analysis. The MNA shortform (MNA-SF) was developed and validated to allow a 2-step screening process. The MNA and MNA-SF are sensitive, specific, and accurate in identifying nutrition risk.


NUTRITIONAL SCREENING
The prevalence of malnutrition in community-dwelling elderly (21 studies, n = 14149 elderly) is 2 +/- 0.1% (mean +/- SE, range 0- 8%) and risk of malnutrition is 24 +/- 0.4% (range 8-76%). A similar pattern is seen in out-patient and home care elderly (25 studies, n = 3119 elderly) with prevalence of undernutrition 9 +/- 0.5% (mean +/- SE, range 0-30%) and risk of malnutrition 45 +/- 0.9% (range 8-65%). A high prevalence of undernutrition has been reported in hospitalized and institutionalized elderly patients: prevalence of malnutrition is 23 +/- 0.5% (mean +/- SE, range 1- 74%) in hospitals (35 studies, n = 8596) and 21 +/- 0.5% (mean +/- SE, range 5-71%) in institutions (32 studies, n = 6821 elderly). An even higher prevalence of risk of malnutrition was observed in the same populations, with 46 +/- 0.5% (range 8-63%) and 51 +/- 0.6% (range 27-70%), respectively. In cognitively impaired elderly subjects (10 studies, n = 2051 elderly subjects), detection using the MNA, prevalence of malnutrition was 15 +/- 0.8% (mean +/- SE, range 0-62%), and 44 +/- 1.1% (range 19-87%) of risk of malnutrition.


CHARACTERISTICS
The large variability is due to differences in level of dependence and health status among the elderly. In hospital settings, a low MNA score is associated with an increase in mortality, prolonged length of stay and greater likelihood of discharge to nursing homes. Malnutrition is associated with functional and cognitive impairment and difficulties eating. The MNA(R) detects risk of malnutrition before severe change in weight or serum proteins occurs.


NUTRITIONAL INTERVENTION
Intervention studies demonstrate that timely intervention can stop weight loss in elderly at risk of malnutrition or undernourished and is associated with improvements in MNA scores. The MNA can also be used as a follow up assessment tool.


CONCLUSION
The MNA is a screening and assessment tool with a reliable scale and clearly defined thresholds, usable by health care professionals. It should be included in the geriatric assessment and is proposed in the minimum data set for nutritional interventions.
Examining students' online interaction in a live video streaming environment using data mining and text mining
(2013)

0747-5632/$ see front matter 2012 Elsevier Ltd. A http://dx.doi.org/10.1016/j.chb.2012.07.020 ⇑ Tel.: +1 757 683 5008; fax: +1 757 683 5639. E-mail address: whe@odu.edu This study analyses the online questions and chat messages automatically recorded by a live video streaming (LVS) system using data mining and text mining techniques. We apply data mining and text mining techniques to analyze two different datasets and then conducted an in-depth correlation analysis for two educational courses with the most online questions and chat messages respectively. The study found the discrepancies as well as similarities in the students’ patterns and themes of participation between online questions (student–instructor interaction) and online chat messages (student–students interaction or peer interaction). The results also identify disciplinary differences in students’ online participation. A correlation is found between the number of online questions students asked and students’ final grades. The data suggests that a combination of using data mining and text mining techniques for a large amount of online learning data can yield considerable insights and reveal valuable patterns in students’ learning behaviors. Limitations with data and text mining were also revealed and discussed in the paper. 2012 Elsevier Ltd. All rights reserved.
Learning to Detect Human-Object Interactions
(2018)

We study the problem of detecting human-object interactions (HOI) in static images, defined as predicting a human and an object bounding box with an interaction class label that connects them. HOI detection is a fundamental problem in computer vision as it provides semantic information about the interactions among the detected objects. We introduce HICO-DET, a new large benchmark for HOI detection, by augmenting the current HICO classification benchmark with instance annotations. To solve the task, we propose Human-Object Region-based Convolutional Neural Networks (HO-RCNN). At the core of our HO-RCNN is the Interaction Pattern, a novel DNN input that characterizes the spatial relations between two bounding boxes. Experiments on HICO-DET demonstrate that our HO-RCNN, by exploiting human-object spatial relations through Interaction Patterns, significantly improves the performance of HOI detection over baseline approaches.
Biohashing: two factor authentication featuring fingerprint data and tokenised random number
(2004)

Human authentication is the security task whose job is to limit access to physical locations or computer network only to those with authorisation. This is done by equipped authorised users with passwords, tokens or using their biometrics. Unfortunately, the first two suffer a lack of security as they are easy being forgotten and stolen; even biometrics also suffers from some inherent limitation and specific security threats. A more practical approach is to combine two or more factor authenticator to reap benefits in security or convenient or both. This paper proposed a novel two factor authenticator based on iterated inner products between tokenised pseudo-random number and the user specific fingerprint feature, which generated from the integrated wavelet and Fourier–Mellin transform, and hence produce a set of user specific compact code that coined as BioHashing. BioHashing highly tolerant of data capture offsets, with same user fingerprint data resulting in highly correlated bitstrings. Moreover, there is no deterministic way to get the user specific code without having both token with random data and user fingerprint feature. This would protect us for instance against biometric fabrication by changing the user specific credential, is as simple as changing the token containing the random data. The BioHashing has significant functional advantages over solely biometrics i.e. zero equal error rate point and clean separation of the genuine and imposter populations, thereby allowing elimination of false accept rates without suffering from increased occurrence of false reject rates. 2004 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.
A weighted additive fuzzy programming approach for multi-criteria supplier selection
(2011)

In supply chain management, to build strategic and strong relationships, firms should select best suppliers by applying appropriate method and selection criteria. In this paper, to handle ambiguity and fuzziness in supplier selection problem effectively, a new weighted additive fuzzy programming approach is developed. Firstly, linguistic values expressed as trapezoidal fuzzy numbers are used to assess the weights of the factors. By applying the distances of each factor between Fuzzy Positive Ideal Rating and Fuzzy Negative Ideal Rating, weights are obtained. Then applying suppliers’ constraints, goals and weights of the factors, a fuzzy multi-objective linear model is developed to overcome the selection problem and assign optimum order quantities to each supplier. The proposed model is explained by a numerical example. 2010 Elsevier Ltd. All rights reserved.
Artificial Neural Networks architectures for stock price prediction: comparisons and applications
(2016)

We present an Artificial Neural Network (ANN) approach to predict stock market indices, particularly with respect to the forecast of their trend movements up or down. Exploiting different Neural Networks architectures, we provide numerical analysis of concrete financial time series. In particular, after a brief résumé of the existing literature on the subject, we consider the Multi-layer Perceptron (MLP), the Convolutional Neural Networks (CNN), and the Long Short-Term Memory (LSTM) recurrent neural networks techniques. We focus on the importance of choosing the correct input features, along with their preprocessing, for the specific learning algorithm one wants to use. Eventually, we consider the S&P500 historical time series, predicting trend on the basis of data from the past days, and proposing a novel approach based on combination of wavelets and CNN, which outperforms the basic neural networks ones. We show, that neural networks are able to predict financial time series movements even trained only on plain time series data and propose more ways to improve results. Key–Words: Artificial neural networks, Multi-layer neural network, Convolutional neural network, Long shortterm memory, Recurrent neural network, Deep Learning, Stock markets analysis, Time series analysis, financial forecasting
MIR_EVAL: A Transparent Implementation of Common MIR Metrics
(2014)

Central to the field of MIR research is the evaluation of algorithms used to extract information from music data. We present mir_eval, an open source software library which provides a transparent and easy-to-use implementation of the most common metrics used to measure the performance of MIR algorithms. In this paper, we enumerate the metrics implemented by mir_eval and quantitatively compare each to existing implementations. When the scores reported by mir_eval differ substantially from the reference, we detail the differences in implementation. We also provide a brief overview of mir_eval’s architecture, design, and intended use. 1. EVALUATING MIR ALGORITHMS Much of the research in Music Information Retrieval (MIR) involves the development of systems that process raw music data to produce semantic information. The goal of these systems is frequently defined as attempting to duplicate the performance of a human listener given the same task [5]. A natural way to determine a system’s effectiveness might be for a human to study the output produced by the system and judge its correctness. However, this would yield only subjective ratings, and would also be extremely timeconsuming when evaluating a system’s output over a large corpus of music. Instead, objective metrics are developed to provide a well-defined way of computing a score which indicates each system’s output’s correctness. These metrics typically involve a heuristically-motivated comparison of the system’s output to a reference which is known to be correct. Over time, certain metrics have become standard for each ∗Please direct correspondence to craffel@gmail.com c © Colin Raffel, Brian McFee, Eric J. Humphrey, Justin Salamon, Oriol Nieto, Dawen Liang, Daniel P. W. Ellis. Licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0). Attribution: Colin Raffel, Brian McFee, Eric J. Humphrey, Justin Salamon, Oriol Nieto, Dawen Liang, Daniel P. W. Ellis.
Fuzzy Cognitive Map software
(2002)

Fuzzy Cognitive Map (FCM) is a soft computing modelling methodology for complex systems. Beyond the mathematical formulation of the FCM theory, there was a need of developing a software tool to facilitate the implementation of FCMs. This paper describes the use of a software tool that was developed to construct FCM models. Some theoretical elements of Fuzzy Cognitive Maps are presented. Then, it is discussed how the software tool is used to develop a model and simulate the use of Fuzzy Cognitive Maps. The user interface of the software tool is described and how the FCM-Analyst is used to facilitate the implementation and simulation of Fuzzy Cognitive Maps.
In the mood: engaging teenagers in psychotherapy using mobile phones
(2011)

Mental illness is a significant and growing problem throughout the world. Many mental health problems have their root in childhood, and early intervention is recommended. Engaging young people in psychotherapeutic activities is challenging, and treatment adherence is often poor. This paper presents a series of studies carried out as part of the development of a mobile and online symptom tracking tool for adolescents with mental health problems. Teenagers use the system to record symptoms on their mobile phones and can view this information in a clinical setting with a therapist. We focus on a clinical pilot of the system with ten users in public mental health clinics. As well as indicating that the mobile diary tool can increase client adherence to therapeutic activities, the study yields insight into the factors influencing the success of the design and informs the design of other systems to be used as adjuncts to therapy.
Lightweight Management of Resource-Constrained Sensor Devices in Internet of Things
(2015)

It is predicted that billions of intelligent devices and networks, such as wireless sensor networks (WSNs), will not be isolated but connected and integrated with computer networks in future Internet of Things (IoT). In order to well maintain those sensor devices, it is often necessary to evolve devices to function correctly by allowing device management (DM) entities to remotely monitor and control devices without consuming significant resources. In this paper, we propose a lightweight RESTful Web service (WS) approach to enable device management of wireless sensor devices. Specifically, motivated by the recent development of IPv6-based open standards for accessing wireless resource-constrained networks, we consider to implement IPv6 over low-power wireless personal area network (6LoWPAN)/routing protocol for low power and lossy network (RPL)/constrained application protocol (CoAP) protocols on sensor devices and propose a CoAP-based DM solution to allow easy access and management of IPv6 sensor devices. By developing a prototype cloud system, we successfully demonstrate the proposed solution in efficient and effective management of wireless sensor devices.
2011 terminology of the vulva of the International Federation for Cervical Pathology and Colposcopy.
(2012)

OBJECTIVE
This study aimed to present the clinical and colposcopic terminology of the vulva (including the anus) of the International Federation of Cervical Pathology and Colposcopy.


MATERIALS AND METHODS
The terminology has been developed by the International Federation of Cervical Pathology and Colposcopy Nomenclature Committee during 2009-2011.


RESULTS
The terminology is part of a comprehensive terminology of the lower genital tract, allowing for standardization of nomenclature by colposcopists, clinicians, and researchers taking care of women with lesions in these areas. The terminology includes basic definitions and normal findings that are important for the clinician lacking experience with management of vulvar disease. This terminology introduces definitions for abnormal findings recently accepted by the International Society for the Study of Vulvovaginal Disease and includes patterns to identify malignancy.


CONCLUSIONS
The terminology differs from past terminologies in that it includes colposcopic patterns and anal colposcopy. Nevertheless, the role of the colposcope in the management of vulvar disease is limited.
Single-Fed Broadband Circularly Polarized Stacked Patch Antenna With Horizontally Meandered Strip for Universal UHF RFID Applications
(2011)

In this paper, a horizontally meandered strip (HMS) feed technique is proposed to achieve good impedance matching and symmetrical broadside radiation patterns for a single-fed broadband circularly polarized stacked patch antenna, which is suitable for universal ultrahigh frequency (UHF) RF identification (RFID) applications. The antenna is composed of two corner truncated patches and an HMS, all of which are printed on the upper side of the FR4 substrates. One end of the HMS is connected to the main patch by a probe, while the other end is connected to an SMA connector. Simulation results are compared with the measurements, and a good agreement is obtained. The measurements show that the antenna has an impedance bandwidth (VSWR <; 1.5) of about 25.8% (758-983 MHz), a 3-dB axial ratio (AR) bandwidth of about 13.5% (838-959 MHz), and a gain level of about 8.6 dBic or larger within the 3-dB AR bandwidth. Therefore, the proposed antenna can be a good candidate for universal UHF RFID readers operating at the UHF band of 840-955 MHz. In addition, a parametric study and a design guideline of the proposed antenna are presented to provide the engineers with information for designing, modifying, and optimizing such an antenna. At last, the proposed antenna is validated in RFID system applications.
Asymmetric-Circular Shaped Slotted Microstrip Antennas for Circular Polarization and RFID Applications
(2010)

Novel asymmetric-circular shaped slotted microstrip patch antennas with slits are proposed for circularly polarized (CP) radiation and radio frequency identification (RFID) reader applications. A single-feed configuration based asymmetric-circular shaped slotted square microstrip patches are adopted to realize the compact circularly polarized microstrip antennas. The asymmetric-circular shaped slot(s) along the diagonal directions are embedded symmetrically onto a square microstrip patch for CP radiation and small antenna size. The CP radiation can be achieved by slightly asymmetric (unbalanced) patch along the diagonal directions by slot areas. Four symmetric-slits are also embedded symmetrically along the orthogonal directions of the asymmetric-circular shaped slotted patch to further reduce antenna size. The operating frequency of the antenna can be tuned by varying the slit length while keeping the CP radiation unchanged. The measured 3-dB axial-ratio (AR) bandwidth of around 6.0 MHz with 17.0 MHz impedance bandwidth is achieved for the antenna on a RO4003C substrate. The overall antenna size is 0.27λo × 0.27λo × 0.0137λo at 900 MHz.
A circularly polarized short backfire antenna excited by an unbalance-fed cross aperture
(2006)

An unbalance-fed cross aperture is developed to excite a short backfire antenna (SBA) for circular polarization. The cross aperture consists of two orthogonal H-shaped slots with a pair of capacitive stubs and is fed by a single probe that forms an unbalanced feed with a shorting pin. It is demonstrated that the cross-aperture-excited SBA can achieve an axial ratio (les 3 dB) bandwidth of 4.2% with a voltage standing wave ratio (VSWR) bandwidth of 6.5% (VSWR<1.2) and a gain of 14 dBi. The antenna structure is described and the simulation and experimental results are presented. The mechanisms for impedance matching and circular-polarization production are analyzed
A Dual-Band Circularly Polarized Aperture-Coupled Stacked Microstrip Antenna for Global Positioning Satellite
(1998)

This paper describes the design and testing of an aperture-coupled circularly polarized antenna for global positioning satellite (GPS) applications. The antenna operates at both the L1 and L2 frequencies of 1575 and 1227 MHz, which is required for differential GPS systems in order to provide maximum positioning accuracy. Electrical performance, lowprofile, and cost were equally important requirements for this antenna. The design procedure is discussed, and measured results are presented. Results from a manufacturing sensitivity analysis are also included.
Wideband Circularly Polarized Patch Antenna Using Broadband Baluns
(2008)

A novel 90deg broadband balun comprising a broadband 90deg Schiffman phase shifter is introduced as a means of enhancing the wideband circular polarization performance of dual-fed type microstrip antennas. The proposed 90deg broadband balun delivers good impedance matching, balanced power splitting and consistent 90deg (plusmn5deg) phase shifting, across a wide bandwidth (~57.5%). A circular patch antenna utilizing the proposed 90deg broadband balun is shown to attain measured impedance(S11< -10 dB) and axial ratio (AR < 3 dB) bandwidths of 60.24% and 37.7%, respectively, for the dual L-probe case; and 71.28% and 81.6% respectively, for the quadruple L-probe case.
vFPGAmanager: A Virtualization Framework for Orchestrated FPGA Accelerator Sharing in 5G Cloud Environments
(2018)

Network operators are actively pushing towards the new 5G era and a crucial part to accomplish this is the Network Functions Virtualization (NFV). FPGAs and their hardware accelerators are a promising solution for NFV and 5G cloud environments because of their fast turnaround time and great speedup potential through application parallelism mapping on the reconfigurable fabric. Recently, consolidation reached a plateau in this field with lightweight virtualization techniques, that require a high overcommitment of FPGA accelerator resources to cope with numerous demands of guests. Although FPGAs can play an important role for the future 5G networks their capability to manage and control them from the upper layers of the software stack is inadequate. The lack of such support coupled with cloud integration and programmability issues can repel potential providers from utilizing FPGAs at their data centers. This paper presents the communication mechanism of the vFPGAmanager, an FPGA virtualization framework which can be orchestrated, monitored and enables accelerators overcommitment with direct guest access. These are key features to allow potential adopters of FPGA technology to include them in the next generation of NFV systems. The communication mechanism architecture is detailed and then benchmarked to show that even under heavy load on the system it demonstrates a minimal overhead to orchestrate and monitor the FPGA as a resource.
Refining Duplicate Detection for Improved Data Quality
(2017)

Detecting duplicates is a pervasive data quality challenge that hinders organizations from extracting value from their data sooner. The increased complexity and heterogeneity of modern datasets has lead to the presence of varying record formats, missing values, and evolving data semantics. As data is integrated, duplicates inevitably occur in the integrated instance. One of the challenges in deduplication is determining whether two values are sufficiently close to be considered equal. Existing similarity functions often rely on counting the number of required edits to transform one value to the other. This is insufficient in attribute domains, such as time, where small syntactic differences do not always translate to ’closeness’. In this paper, we propose a duplication detection framework, which adapts metric functional dependencies (MFDs) to improve the detection accuracy by relaxing the matching condition on numeric values to allow a permitted tolerance. We evaluate our techniques against two existing approaches using three real data collections, and show that we achieve an average 25% and 34% improvement in precision and recall, respectively, over non-MFD versions.